code: def extract_features(
      self, embedded_tokens: torch.Tensor, padding_mask: torch.Tensor
  ):
      # support passing in a single sentence
      embedded_tokens = embedded_tokens.view(
          -1, embedded_tokens.shape[-2], embedded_tokens.shape[-1]
      )
      return self.transformer(embedded_tokens, padding_mask[0])
  
imports: import re
  from typing import List, Optional
  import torch
  from pytext.utils.usage import log_class_usage
  from pytorch.text.fb.nn.modules.transformer import Transformer
  from torch import nn
label_desc: Use image node_id 2 for calling the PassthroughEncoder obj's underlying member method code with example usage: obj.extract_features(embedded_tokens, padding_mask) and returns: self